A person search system, which allows a computer to search for a target person from video images (moving images) captured or recorded by a surveillance camera or the like, using an image recognition technology or the like, has been known conventionally (for example, see Patent Literatures 1 to 4 and Non Patent Literature 1). Such a technology of performing searching based on features of an image itself, without depending on external information such as tagging, is generally called CBIR (Content-Based Image Retrieval) which has been started to be also used for searching for a person.
Patent Literature 2 discloses a video image search system and a person search method in which a portion where (the face of) a person is cut out from an image, and a color histogram or the like is extracted as a feature value for identifying the person individually, and when the feature value is similar to that of a target person, it is estimated that they are the same person.
Feature values used for image recognition are luminance values of pixels like Eigenface used in main component analysis in old days. As the first generation feature values, those based on distribution of pixel values (luminance) and wavelet transformation have been known. In the second generation, feature values based on a local region such as Haar-Like, HOG, EOH, and Edgelet have been known, and also, those having scale invariant property regarding a focused feature point like SIFT and SURF have been known. In the third generation, learning is made in consideration of spatial relativeness of those local regions, and Joint Haar-like, Joint HOG, sparse feature, Shapelet, co-occurrence probability feature, and the like have been used.
In recent years, a time space feature such as PSA (Pixel State Analysis), as the four generation, has been researched (for example, see Non Patent Literature 2).
The performance of face recognition depends of a face image databased used for learning. FERET face database, often used in a contest, includes a right-sided face and a left-sided face, besides a front face. Regarding this database, face recognition having a FRR (False Reject Rate) of about 0.0003%, with respect to FAR (False Accept Rate)=0.001, is realized at the time of year 2010.
CBIR having an accuracy of a practical level in face recognition is expected to be used in various fields in the future.
As an example thereof, a personal safety verification system in times of disaster or the like is considered.
A safety verification means in times of disaster, which is currently used widely, is provided by a telecommunications carrier. For example, a person who wishes to verify safety (verification requesting person) or a person who is to be verified (not verification requesting person) calls a predetermined telephone number, remains a voice message with a telephone number of the other person or the own, and then the other person calls a predetermined telephone number an inputs the telephone number of the own or the other person, whereby the voice message can be played.
Further, there are also various means such as one in which safety information is input using characters from a mobile phone, a smart phone, PC, or the like, and the other person is able to search for and view the safety information using a telephone number or the like, and one in which voice can also be recorded and played with a similar operation. There is also a means for automatically send an email prompting inputting of safety information to an email address of a person to be verified.
Further, SNS (Social Networking services) and the like, which are widely used recently, may also be used as safety verification means.
It should be noted that relating to the present invention, a personal safety verification system using CBIR technology, and one in which any kind of personal information and a telephone number are paired to be used for searching, for example, have been known (see Patent Literatures 5 to 7, for example).
Further, a technology of performing identification of a human body using images has been known (see Patent Literatures 8 and 9, for example).